#artificialintelligence #ai #technology #futuretech
This change will revolutionize leadership, governance, and workforce development. Successful firms will invest in technology and human capital by reskilling personnel, redefining roles, and fostering a culture of human-machine collaboration.
The Imperative of Strategy Artificial intelligence is not preordained; it is a tool shaped by human choices. How we execute, regulate, and protect AI will determine its impact on industries, economies, and society. I emphasized in Inside Cyber that technology convergence—particularly the amalgamation of AI with 5G, IoT, distributed architectures, and ultimately quantum computing—will augment both potential and hazards.
The issue at hand is not if AI will transform industries—it has already done so. The essential question is whether we can guide this change to enhance security, resilience, and human well-being. Individuals who interact with AI strategically, ethically, and with a long-term perspective will gain a competitive advantage and foster the advancement of a more innovative and secure future.
Researchers have created atomically thin artificial neurons capable of processing both light and electric signals for computing. The material enables the simultaneous existence of separate feedforward and feedback paths within a neural network, boosting the ability to solve complex problems.
For decades, scientists have been investigating how to recreate the versatile computational capabilities of biological neurons to develop faster and more energy-efficient machine learning systems. One promising approach involves the use of memristors: electronic components capable of storing a value by modifying their conductance and then utilising that value for in-memory processing.
However, a key challenge to replicating the complex processes of biological neurons and brains using memristors has been the difficulty in integrating both feedforward and feedback neuronal signals. These mechanisms underpin our cognitive ability to learn complex tasks, using rewards and errors.
Walter (Dated: December 22, 2025)
See… https://osf.io/preprints/psyarxiv/c5gh8_v1
Abstract: Large language models (LLMs) are widely described as artificial intelligence, yet their epistemic profile diverges sharply from human cognition. Here we show that the apparent alignment between human and machine outputs conceals a deeper structural mismatch in how judgments are produced. Tracing the historical shift from symbolic AI and information filtering systems to large-scale generative transformers, we argue that LLMs are not epistemic agents but stochastic pattern-completion systems, formally describable as walks on high-dimensional graphs of linguistic transitions rather than as systems that form beliefs or models of the world. By systematically mapping human and artificial epistemic pipelines, we identify seven epistemic fault lines, divergences in grounding, parsing, experience, motivation, causal reasoning, metacognition, and value. We call the resulting condition Epistemia: a structural situation in which linguistic plausibility substitutes for epistemic evaluation, producing the feeling of knowing without the labor of judgment. We conclude by outlining consequences for evaluation, governance, and epistemic literacy in societies increasingly organizedaround generative.
Cc: ronald cicurel ernest davis amitā kapoor darius burschka william hsu moshe vardi luis lamb jelel ezzine amit sheth bernard W. kobes.
See…
TRENDS Research & Advisory strives to present an insightful and informed view of global issues and challenges from a strategic perspective. Established in 2014 as an independent research center, TRENDS conducts specialized studies in the fields of international relations and political, economic and social sciences.
In the 21st century, new powerful technologies, such as different artificial intelligence (AI) agents, have become omnipresent and the center of public debate. With the increasing fear of AI agents replacing humans, there are discussions about whether individuals should strive to enhance themselves. For instance, the philosophical movement Transhumanism proposes the broad enhancement of human characteristics such as cognitive abilities, personality, and moral values (e.g., Grassie and Hansell 2011; Ranisch and Sorgner 2014). This enhancement should help humans to overcome their natural limitations and to keep up with powerful technologies that are increasingly present in today’s world (see Ranisch and Sorgner 2014). In the present article, we focus on one of the most frequently discussed forms of enhancement—the enhancement of human cognitive abilities.
Not only in science but also among the general population, cognitive enhancement, such as increasing one’s intelligence or working memory capacity, has been a frequently debated topic for many years (see Pauen 2019). Thus, a lot of psychological and neuroscientific research investigated different methods to increase cognitive abilities, but—so far—effective methods for cognitive enhancement are lacking (Jaušovec and Pahor 2017). Nevertheless, multiple different (and partly new) technologies that promise an enhancement of cognition are available to the general public. Transhumanists especially promote the application of brain stimulation techniques, smart drugs, or gene editing for cognitive enhancement (e.g., Bostrom and Sandberg 2009). Importantly, only little is known about the characteristics of individuals who would use such enhancement methods to improve their cognition. Thus, in the present study, we investigated different predictors of the acceptance of multiple widely-discussed enhancement methods. More specifically, we tested whether individuals’ psychometrically measured intelligence, self-estimated intelligence, implicit theories about intelligence, personality (Big Five and Dark Triad traits), and specific interests (science-fiction hobbyism) as well as values (purity norms) predict their acceptance of cognitive enhancement (i.e., whether they would use such methods to enhance their cognition).
https://www.jimruttshow.com/currents-ben-goertzel-2/
Jim talks with Ben Goertzel about the ideas in his recent essay “Three Viable Paths to True AGI.” They discuss the meaning of artificial general intelligence, Steve Wozniak’s basic AGI test, whether common tasks actually require AGI, a conversation with Joscha Bach, why deep neural nets are unsuited for human-level AGI, the challenge of extrapolating world-models, why imaginative improvisation might not be interesting to corporations, the 3 approaches that might have merit (cognition-level, brain-level, and chemistry-level), the OpenCog system Ben is working on, whether it’s a case of “good old-fashioned AI,” where evolution fits into the approach, why deep neural nets aren’t brain simulations & attempts to make them more realistic, a hypothesis about how to improve generalization, neural nets for music & the psychological landscape of AGI research, algorithmic chemistry & the origins of life problem, why AGI deserves more resources than it’s getting, why we may need better parallel architectures, how & how much society should invest in new approaches, the possibility of a cultural shift toward AGI viability, and much more.
